**Architectural Logic**: Production Spark patterns require config, partitioning, and join optimization.
```python
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ETL")\
.config("spark.sql.adaptive.enabled", "true")\
.getOrCreate()
df1 = spark.read.option("header", True).csv("s3://bucket/orders.csv")
df2 = spark.read.option("header", True).csv("s3://bucket/customers.csv")
joined = df1.join(broadcast(df2), df1.customer_id == df2.id, "left")
joined.write.partitionBy(...
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like FedEx Dataworks. The answer also includes follow-up discussion points that interviewers commonly explore.
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